abstract = "Metaheuristic optimisation procedures such as
Evolutionary Algorithms are usually driven by an
objective function which rates the quality of a
candidate solution. However, it is not clear in
practice whether an objective function adequately
rewards intermediate solutions on the path to the
global optimum and it may exhibit deceptiveness,
epistasis, neutrality, ruggedness, and a lack of
causality. In this paper, we introduce the Frequency
Fitness H, subject to minimisation, that rates how
often solutions with the same objective value have been
discovered so far. The ideas behind this method are
that good solutions are hard to find and that if an
algorithm gets stuck at a local optimum, the frequency
of the objective values of the surrounding solutions
will increase over time, which will eventually allow it
to leave that region again. We substitute a Frequency
Fitness Assignment process (FFA) for the objective
function into several different optimisation
algorithms. We conduct a comprehensive set of
experiments: the synthesis of algorithms with Genetic
Programming (GP), the solution of MAX-3SAT problems
with Genetic Algorithms, classification with Memetic
Genetic Programming, and numerical optimisation with a
(1+1) Evolution Strategy, in order to verify the
utility of FFA. Given that they have no access to the
original objective function at all, it is surprising
that for some problems (e.g., the algorithm synthesis
task) the FFA-based algorithm variants perform
significantly better. However, this cannot be
guaranteed for all tested problems. We thus also
analyse scenarios where algorithms using FFA do not
perform better or even worse than with the original
objective functions.",